CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN AGRA

Live Virtual

Instructor Led Live Online

110,000
63,945

  • IABAC® & JAINx® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
36,645

  • IABAC® & JAINx® Certification
  • One year access to Self Learning
  • 10 Capstone Projects
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Classroom

In - Person Classroom Training

110,000
69,195

  • IABAC® & JAINx® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Classroom Training
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

Enquire Now

UPCOMING DATA ENGINEER ONLINE CLASSES IN AGRA

BEST CERTIFIED DATA ENGINEER CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

images not display images not display

WHY DATAMITES INSTITUTE FOR DATA ENGINEER COURSE

Why DataMites Infographic

SYLLABUS OF DATA ENGINEER CERTIFICATION IN AGRA

MODULE 1: DATA ENGINEERING INTRODUCTION

• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools and platforms
• Core concepts of Data engineering

MODULE 2: DATA SOURCES AND DATA IMPORT

• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data

MODULE 3: DATA PROCESSING

• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data wrangling with Pandas
• Managing large data sets with Pandas
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas

MODULE 4: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data Ingestion through Pandas methods
• Hands-on: Ingestion, Transform Data and Load data

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

MODULE 1: DATA ENGINEERING INTRODUCTION

• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools, and platforms
• Core concepts of Data engineering

MODULE 2: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart — Know the Difference
• Data Lake Introduction
• Data Lake Architecture
• Data Warehouse vs Data Lake

MODULE 3: DATA SOURCES AND DATA IMPORT

• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data

MODULE 4: DATA PROCESSING

• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas

MODULE 5: DOCKER AND KUBERNETES FOUNDATION

• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping, and managing images)
• Publishing containers to DockerHub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 6: DATA ORCHESTRATION WITH APACHE AIRFLOW

• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow

MODULE 7: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines

MODULE 1 : AWS DATA SERVICES INTRODUCTION 

  • AWS Overview and Account Setup
  • AWS IAM Users, Roles and Policies
  • AWS Lamdba overview
  • AWS Glue overview
  • AWS Kinesis overview
  • AWS Dynamodb overview
  • AWS Anthena overview
  • AWS Redshift overview

MODULE 2 : DATA INGESTION USING AWS LAMDBA 

  • Setup AWS Lamdba  local development env
  • Deploy project to Lamdba console
  • Data pipeline setup with Lamdba
  • Validating data files incrementally
  • Deploying Lamdba function

MODULE 3 : DATA PIPELINE WITH AWS KINESIS 

  • AWS Kinesis overview and setup
  • Data Streams with AWS Kinesis
  • Data Ingesting from AWS S3 using AWS Kinesis

MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT 

  • AWS Redshift Overview
  • Analyze data using AWS Redshift from warehouses, data lakes and operations DBs
  • Develop Applications using AWS Redshift cluster
  • AWS Redshift federated Queries and Spectrum

MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE 

  • Azure Synapse setup
  • Understanding Data control flow with ADF
  • Data Pipelines with Azure Synapse
  • Prepare and transform data with Azure Synapse Analytics

MODULE 6 : STORAGE IN AZURE 

  • Create Azure storage account
  • Connect App to Azure Storage
  • Azure Blog Storage

MODULE 7: AZURE DATA FACTORY

  • Azure Data Factory Introduction
  • Data transformation with Data Factory
  • Data Wrangling with Data Factory

MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS

  • Hands-on Project Case-study
  • Setup Project Development Env
  • Organization of Data Sources
  • AZURE/AWS services for Data Ingestion
  • Data Extraction Transformation  

MODULE 1: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart — Know the Difference
• Data Lake Introduction
• Data Lake Architecture
• Data Warehouse vs Data Lake

MODULE 2: DOCKER FOUNDATION

• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping and managing images)
• Publishing containers to Docker Hub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 3: KUBERNETES CONTAINER ORCHESTRATION

• Kubernetes Introduction
• Setting up Kubernetes Clusters
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 4: DATA ORCHESTRATION WITH APACHE AIRFLOW

• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow

MODULE 5: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines

MODULE 1 : DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2 : SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

MODULE 3 : DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment

MODULE 4 : DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table

MODULE 5 : SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join

MODULE 6 : SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

DATA ENGINEER TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ENGINEER TRAINING IN AGRA

The ever-expanding big data market is poised to reach a staggering $229.4 billion by 2025, fuelled by a remarkable CAGR of 10.9%. (MarketsandMarkets). This surge is driven by the increasing volume, variety, and velocity of data generated across industries. With the need for streamlined data management and processing, data engineering plays a pivotal role in ensuring data's value and transforming it into actionable insights. As businesses embrace the power of big data, the demand for skilled data engineers continues to soar, presenting abundant opportunities for professionals in this thriving field.

Embark on a transformative journey with DataMites' Data Engineer course in Agra. This comprehensive program spans over 6 months, encompassing more than 150 learning hours. With 50+ hours of live online training, participants gain real-time insights from industry experts. Engaging in 10 capstone projects and a client project, learners apply their skills to solve practical challenges. The course also offers a 365-day Flexi Pass and Cloud Lab access for continuous learning. Additionally, DataMites provides offline courses on demand for those seeking in-person training in Agra.

10 Reasons to Choose DataMites for Data Engineer Training in Agra:

  • Guidance of Ashok Veda and Expert Faculty: Learn from industry stalwart Ashok Veda and a team of experienced faculty members.

  • Comprehensive Course Curriculum: Gain in-depth knowledge through a comprehensive curriculum covering all aspects of data engineering.

  • Global Certification: Earn globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing your professional credibility.

  • Flexible Learning Options: Choose between online data engineer course in Agra and ON DEMAND data engineer offline training in Agra modes to suit your schedule and learning preferences.

  • Real-world Projects: Work on projects using real-world datasets to acquire hands-on experience and practical skills.

  • Internship Opportunities: Gain practical exposure and industry experience through data engineer internship programs.

  • Placement Assistance and Job References: Receive dedicated support for data engineer course with placements and valuable job references.

  • Hardcopy Learning Materials and Books: Access high-quality hardcopy learning materials and books for a comprehensive learning experience.

  • DataMites Exclusive Learning Community: Join a vibrant learning community, interact with peers, and expand your professional network.

  • Affordable Pricing and Scholarships: Benefit from affordable pricing options and explore scholarship opportunities to make the course accessible to a wider audience.

DataMites offers a recognized Data Engineer Certification in Agra, validating your proficiency in data engineering. Agra, renowned for its architectural marvels like the Taj Mahal, presents a unique blend of history and modernity. The city boasts several educational institutions and a growing IT sector, making it an ideal destination for skill development in data engineering. Immerse yourself in the rich cultural heritage of Agra while gaining industry-relevant knowledge to excel in the field of data engineering.

Along with the data engineer courses, DataMites also provides python training, tableau, deep learning, data analytics, mlops, IoT, artificial intelligence, AI expert, data mining, data science, r programming, data analyst and machine learning courses in Agra.

ABOUT DATA ENGINEER COURSE IN AGRA

Data engineering encompasses the utilization of engineering principles and methods to manage the complete data lifecycle, covering activities such as data collection, ingestion, storage, processing, integration, and delivery. Its primary emphasis is on achieving scalability, reliability, and efficiency.

To pursue a career as a data engineer in Agra, individuals can follow these steps:

a. Obtain a degree in computer science, engineering, or a related field.

b. Develop proficiency in programming languages such as Python, SQL, or Java.

c. Gain knowledge of database management systems and data processing frameworks like Hadoop and Spark.

d. Acquire practical experience through internships, projects, or working on data-related tasks.

e. Continuously update skills by staying informed about emerging technologies and industry trends.

It is indeed feasible to transition from a mechanical background to data engineering. While having a computer science or related field background can facilitate the process, individuals can bridge the gap by acquiring essential skills like programming, database management, and data processing. Consider exploring training programs or obtaining certifications in data engineering to further enhance your prospects.

Emerging advancements and shifts in data engineering include the rise of cloud-based solutions, such as AWS and Azure, for scalable and flexible data management. Big data processing frameworks like Apache Spark and Hadoop are becoming more prevalent. Real-time data streaming with technologies like Apache Kafka and Flink is gaining importance. Integration of DataOps and DevOps methodologies is becoming widespread, promoting agility in data management. Additionally, machine learning and AI are being incorporated into data engineering workflows for enhanced data processing and analysis capabilities.

The future prospects for individuals building a career as data engineers are highly promising. With the ever-growing volume and complexity of data being generated, organizations are seeking skilled professionals who can effectively manage, process, and analyze data. The rapid advancements in technology, coupled with the increasing importance of data-driven insights, create a favorable landscape for data engineers. By continually upgrading their skills and staying abreast of emerging technologies, data engineers can expect ample career opportunities and professional growth.

The training fees for data engineer programs in Agra can differ based on factors like the training institute, course duration, and mode of training (online or classroom). Generally, the cost can range between 40,000 INR to INR 1,00,000. To get accurate information, it's advisable to explore various training providers in Agra and inquire about the specific costs associated with their data engineer training courses.

Considered among the top providers of Data Engineer Training, DataMites stands out due to its well-designed curriculum, practical projects aligned with industry needs, and knowledgeable trainers. DataMites has a proven track record of delivering high-quality education that prepares individuals to excel in the dynamic field of data engineering.

Upon completion of Data Engineer Training in Agra, individuals can expect promising job prospects. They can pursue careers as Data Engineers, Data Analysts, Data Scientists, Database Administrators, or Business Intelligence Developers.

Fundamental skills for success as a data engineer include proficiency in programming languages like Python or Java, expertise in database management (SQL, NoSQL), familiarity with big data processing frameworks (Hadoop, Spark), strong grasp of data integration and ETL processes, and the ability to design and optimize data architectures.

The average salary range for Data Engineers in Agra can vary depending on factors such as experience, skills, industry, and the organization's size.  Generally, the average salary range for Data Engineers in Agra falls between INR 3,00,000 to INR 8,00,000 per annum.

FAQ’S OF DATA ENGINEER COURSE IN AGRA

DataMites offers training programs designed to meet industry demands, featuring experienced instructors, practical projects, and hands-on learning opportunities. By enrolling in these programs, individuals can acquire the skills and knowledge essential for excelling in the field of data engineering.

The curriculum of the DataMites Certified Data Engineer Training program in Agra includes specific topics such as data engineering fundamentals, database management, data warehousing, ETL processes, big data processing frameworks, data visualization, and advanced analytics techniques.

The length of the DataMites Data Engineer Course in Agra is flexible and depends on the learning mode selected. Generally, online instructor-led training lasts for approximately 6 months, involving over 150 learning hours. Keep in mind that the duration may differ for self-paced learning alternatives.

DataMites offers Data Engineer Training in Agra at prices that can fluctuate based on factors like the specific program chosen, training mode (online or classroom), and any added resources or features. The fees for the data engineer course at DataMites in Agra typically span from approximately INR 26,548 to INR 68,000, depending on the program and any supplementary components incorporated.

DataMites' Flexi-Pass provides learners with the advantage of attending multiple batches of the same course within a designated period. This enables learners to strengthen their grasp of the course content, revisit important concepts, and solidify their understanding of the subject matter, thereby enhancing their overall learning experience.

Generally, eligibility for the Data Engineer Course at DataMites in Agra requires an educational background in computer science, engineering, mathematics, or a related field.

Yes, upon successfully completing Data Engineer training at DataMites, participants will be granted certifications. DataMites has partnerships with prestigious organizations like the International Association of Business Analytics Certifications (IABAC), NASSCOM FutureSkills Prime, and Jain (Deemed-to-be University). These collaborations ensure that the training programs meet industry requirements and offer certifications that hold value.

If a participant misses a session during Data Engineer training at DataMites, they generally have access to recorded sessions or can attend makeup sessions at a later time. DataMites' objective is to ensure that learners have the means to make up for missed content and progress effectively in their learning journey.

Yes, DataMites typically provides the option to attend a demo class prior to making the course fee payment. This allows individuals to gauge the teaching methodology, interact with instructors, and have a preview of the course content and structure. Participating in a demo class assists in making an educated decision about enrolling in the training program.

DataMites provides both classroom and online training options for Data Engineer courses in Agra. Participants have the flexibility to choose the training mode that suits their needs and preferences. Regardless of the chosen mode, DataMites delivers comprehensive instruction and hands-on learning experiences to foster proficiency in data engineering skills.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

View more

DATA ENGINEER PROJECTS

DATA ENGINEER JOB INTERVIEW QUESTIONS

OTHER DATA ENGINEER TRAINING CITIES IN INDIA

Global CERTIFIED DATA ENGINEER COURSES Countries

popular career ORIENTED COURSES

DATAMITES POPULAR COURSES


HELPFUL RESOURCES - DataMites Official Blog